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Title |
Design Artificial Neural Network Using FPGA |
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Author |
Haitham Kareem Ali, Esraa Zeki Mohammed |
| Citation |
Vol. 10 No. 8 pp. 88-92 |
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Abstract |
In the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL). |
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Keywords |
Artificial Neural Network, Hardware Description Language, Field Programmable Gate Arrays (FPGAs), Sigmoid Activation Function |
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